Resource Center

Reader Services

Editors' Cuts

The Evolving Arc of Analytics

One useful metric for charting the ascendancy of predictive analytic technologies is to survey the financial results of SAS Institute Inc.

In fiscal year 2010, the Cary, N.C.-based firm recorded global revenue of $2.43 billion, up 5.2% over the previous year. In the business analytics category, revenue rose some 26%. The results marked the 35th consecutive year of revenue growth for the company.

Yet numbers alone are insufficient to elucidate just how broad the use of predictive analysis has become. In addition to traditional strongholds such as financial services, the technology is increasingly finding uses in sectors such as retail. A predictive program might help a retailer optimize items for markdown, while another may aid a credit card processor score the purchases in real-time to determine if fraudulent.

Despite the growing ubiquity of analytics, SAS co-founder and CEO Jim Goodnight still sees many new areas where analytics can help businesses solve problems. “Even though insurers and bankers have been at it for a while, I think we’re still at a very early stage,” Goodnight tells Insurance Networking News.

Part of the continuing evolution, Goodnight says it is providing results faster to make them more actionable by the business. In the case of insurers looking to hedge financial risk, having instantaneous analytics results can have major implications by giving insurers a better sense of which market instruments to buy beforehand. “All risk calculations now are done after the fact,” Goodnight says. “We’re working hard to bring assessment of market risk and credit risk down to under an hour. We’re almost to the point where you can do it in real time.”

However, this road to real-time analytics is only feasible in light of concurrent advances in processing power. As the limits of the photolithography and silicon itself have forced chipmakers to forsake higher clock speeds for multi-core designs, software makers were forced to adjust as a result. Goodnight says the company has spent the past seven or eight years optimizing its offerings to run on multi-core hardware. “We had to find ways to rewrite our software to make use of all those cores. That means changing the sequential process we’ve been used to writing as programmers for the past 40 years and now getting things to run in parallel.”

Much as the exponential increase in processing power has introduced both opportunities and challenges, the similar explosion in storage capacity is a double-edged sword. “The problem analytics faces now is that the volumes of data are growing so rapidly,” Goodnight says. “We’re looking at the amount of data in the world doubling annually.” Indeed, in an era when every Internet search is saved and thus potential grist for predictive analysis, separating data with predictive value from the ocean of digital dross is no mean feat. Accordingly, Goodnight says the company is working with its customers to better determine what data to save and what to throw out. “Our biggest long-range problem is how to deal with more data,” he says.

Bill Kenealy is a senior editor with Insurance Networking News.

Readers are encouraged to respond to Bill by using the “Add Your Comments” box below. He can also be reached at william.kenealy@sourcemedia.com.

This blog was exclusively written for Insurance Networking News. It may not be reposted or reused without permission from Insurance Networking News.